Wound Care Recommendation Using Image-Based Dressing Case Matching
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Solution Overview
Problem
The challenge of providing accurate and efficient wound care recommendations is hindered by the complexity of dressing selection, lack of trained wound experts, and the burden of long-distance medical examinations for patients, particularly in rural areas, leading to prolonged healing times and increased medical costs.
Innovation Solution
A care plan recommendation system and method that utilizes a storage device, image capturing device, and processor to analyze wound images, filter similar case data, calculate recovery metrics, and generate dressing recommendations using a Generative Pre-training Transformer (GPT) process to provide care method and life note texts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If wound experts provide care, then care quality is improved, but training cost and time are significantly increased
Solution Approach 1:
The system creates a digital copy of wound expert knowledge through the wound care knowledge base, storing dressing selection criteria, wound assessment guidelines, and care protocols. This knowledge base serves as a reusable template that can guide any caregiver without requiring them to undergo years of specialized training, effectively copying expert decision-making capabilities into an accessible digital format.
Solution Approach 2:
The intelligent system acts as an intermediary between wound experts and general caregivers. It translates complex expert knowledge into simplified, actionable recommendations that general nurses and caregivers can implement. The system mediates the knowledge gap by processing wound images and patient data to generate care plans that reflect expert-level decision-making without requiring expert-level training.
2Reliability
If multiple dressing types are used for different wounds, then treatment effectiveness is improved, but system complexity is significantly increased
Solution Approach 1:
The system segments the complex dressing selection process into distinct analytical components: wound type identification, wound stage assessment, infection risk evaluation, and dressing recommendation. Each component processes specific aspects of the wound independently, then integrates results to provide comprehensive dressing recommendations. This segmentation transforms an overwhelming holistic decision into manageable sequential steps.
Solution Approach 2:
The system applies different assessment criteria and dressing recommendations tailored to specific wound types and stages. Rather than using a uniform approach for all wounds, it customizes the care plan according to the local characteristics of each wound - whether it's a pressure ulcer, diabetic wound, or surgical incision, and whether it's in the acute, subacute, or chronic stage.
3Reliability
If regular clinic visits are required for wound debridement, then wound care quality is improved, but patient burden and medical costs are significantly increased
Solution Approach 1:
The system enables patients and caregivers to perform wound assessment and receive care recommendations independently through the intelligent analysis platform. Users can capture wound images, input patient information, and receive automated dressing recommendations without requiring travel to the clinic. This self-service capability maintains care quality by providing expert-level guidance while eliminating the time and logistical burden of regular clinic visits.
Solution Approach 2:
The system performs preliminary wound assessment and dressing recommendation before the patient needs to visit the clinic. By analyzing wound images and providing care plans in advance, it prepares patients and caregivers with the knowledge needed to perform appropriate wound care at home, reducing the frequency and urgency of clinic visits while maintaining treatment effectiveness.
Data Source
AI summary
A care plan recommendation method includes: capturing a wound image of a wound; analyzing a wound status of the wound based on the wound image; filtering out similar case data from wound case data based on at least one filtering condition according to the wound status; summarizing into dressing types according to a part of the similar case data in the similar case data; calculating average recovery days, average recovery cost and recovery cost standard deviation corresponding to at least one dressing item in each dressing type according to at least part of the similar case data associated with each of the dressing types, and generating and displaying a dressing usage result; searching for keywords from the at least part of the similar case data associated with each dressing type, and converting the keywords to generate and display a care method text and a life note text through GPT.